🔍 Read the full analysis: AI Enhancing Knowledge Work Efficiency on ThorstenMeyerAI.com
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TL;DR
A Nov. 10, 2026, essay from ThorstenMeyerAI.com uses historical research to describe the rise of knowledge and information work, the part of the economy now targeted by generative AI. It cites estimates of broad AI exposure but says exposure does not establish job losses; overall U.S. employment data has not shown a clear AI effect so far.
ThorstenMeyerAI.com published an essay on November 10, 2026, tracing the growth of knowledge and information work and arguing that it is the part of the economy now most directly exposed to generative AI. The essay points to historical estimates that knowledge industries accounted for nearly 29% of adjusted U.S. gross national product in 1958, while cautioning that AI exposure is not proof of job losses.
The essay revisits economist Fritz Machlup’s 1962 study, which estimated that the production and distribution of knowledge cost $136.4 billion in 1958, or “almost 29 per cent of adjusted GNP.” Machlup counted spending across education, research and development, media, information machines and information services. That figure measures expenditure, not the market value of knowledge itself.
It also draws on Marc Porat’s 1977 analysis for the U.S. Department of Commerce. Porat classified workers by the tasks they performed rather than the industry where they worked. In his estimates, information workers rose from 5.8% of the U.S. workforce in 1860 to 46.4% in 1970; his report said the information group had become the largest by the mid-1950s.
For a current comparison, the essay cites 2025 International Labour Organization figures showing managers, professionals and technicians held 62% of jobs in Singapore, 52.5% in the United States and 47.9% in Germany. It says higher-income and clerical roles are among those at the front of the line for generative AI exposure. These figures describe occupational composition and exposure, not the number of jobs already eliminated by AI.
Infographic 01 · The number to remember
Fritz Machlup's first count of the knowledge industries, by branch.
US spending on knowledge by branch, 1958 · share of $136.4 bn
Machlup measured what knowledge cost (spending, the way national accounts count), not what it was worth. “Almost 29 per cent of adjusted GNP” is his figure for 1958. The branch values are as tabulated by Godin (2008); the labour shares are Machlup’s estimate as summarised by his publisher.
Sources: F. Machlup, The Production and Distribution of Knowledge in the United States (Princeton University Press, 1962), as tabulated by B. Godin, “The Knowledge Economy: Fritz Machlup's Construction of a Synthetic Concept” (2008); Princeton University Press, book description.
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AI Reaches the Office Workforce
The development matters because many AI discussions focus on future capabilities, while the essay centers the people and tasks that make up a large part of modern economies. Reading, writing, planning, calculating and checking are routine activities in knowledge-intensive jobs; tools that can assist with them may affect how work is organized, even when a worker’s job title does not change.
But exposure is not displacement. A task may be assisted, accelerated or reorganized without an entire role disappearing. The essay says overall U.S. employment data has not yet shown a clear AI footprint. It also reports that young workers in the most exposed jobs are falling behind their peers, but provides no underlying measure or evidence that attributes that gap to AI. That observation warrants attention, not a conclusion that AI caused the difference.
The economic stakes are broad: employers may change staffing, training and productivity expectations, while workers may need to adapt to tools that handle parts of their work. The historical figures show that information work became a major part of the economy long before generative AI; they do not by themselves predict the scale or speed of changes ahead.
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How Information Work Was Counted
In 1959, management writer Peter Drucker used the term “knowledge worker” for people whose livelihoods depend on what they know rather than primarily on manual labor. His argument, as presented in the source essay, was that expanding knowledge work would call for different management practices from those developed for factory production.
Machlup then tried to quantify knowledge-related activity by counting its costs, including education and communications. Porat later classified workers according to what they did during the workday. The approaches answer different questions: Machlup estimated spending on knowledge industries, while Porat estimated the workforce and economic activity associated with information work. Neither historical measure is a direct forecast of AI’s effects.
The essay frames information work as a fourth economic sector alongside agriculture, industry and services, sometimes called the quaternary sector. Its key historical point is that office and information work had become economically prominent decades before current AI systems began targeting tasks such as drafting, summarizing and analysis.
““Exposure is not job loss, yet.””
— ThorstenMeyerAI.com essay
The Employment Effects Remain Unclear
The source material does not specify which studies or methods underpin its statements about generative AI exposure, or how exposure is measured across occupations. It also gives no figures for the reported gap affecting young workers in highly exposed jobs, including its size, time period or comparison group. The stated gap therefore cannot establish that AI caused those workers to fall behind.
It remains unclear how much AI will change staffing, wages, output or the content of specific jobs. The historical statistics provide context about the scale of knowledge work, not evidence that current systems can perform every task within it. No causal finding or definitive estimate of net job losses is provided.
Watch for Evidence on Jobs
The next meaningful evidence will come from employment and workplace data that can distinguish AI-related changes from other economic forces. Researchers and policymakers will need to track hiring, layoffs, wages, job duties and outcomes for workers in roles with different levels of exposure over time.
The essay presents no new labor-market forecast, announced policy or scheduled research release. For now, its contribution is a historical frame: knowledge work has grown into a major part of the economy, and AI tools are aimed at many of its tasks. Whether that changes the number or quality of jobs remains an open question.
Key Questions
What is the news development?
ThorstenMeyerAI.com published an essay on Nov. 10, 2026, linking the long-term growth of knowledge work to current generative AI exposure estimates.
What did Machlup estimate?
Machlup estimated that U.S. spending on producing and distributing knowledge was $136.4 billion in 1958, almost 29% of adjusted GNP. The figure measures costs, not the total value of knowledge.
Does AI exposure mean a job will disappear?
No. Exposure indicates that work may include tasks AI systems could affect; it does not show that an entire job will be eliminated. The source says overall U.S. employment data has not shown a clear AI footprint so far.
What did Porat find about information workers?
Porat’s 1977 report estimated that information workers made up 46.4% of the U.S. workforce in 1970, up from 5.8% in 1860. These are historical estimates based on his classification of workers by their tasks.
What remains unknown about AI’s effect on workers?
The scale of any changes to employment, pay and job duties is not established in the source material. It also does not provide details or causal evidence for its claim that young workers in highly exposed jobs are falling behind peers.
Source: ThorstenMeyerAI.com
